Data-driven extraction of the substructure of quark and gluon jets in proton-proton and heavy-ion collisions
Name
Ying-kying-meng-eecs-2022-thesis.pdf
Description
Thesis PDF
Size
1.77 MB
Format
Adobe PDF
Checksum (MD5)
89906df7e01d3892f3ea886b49d3d18a
Author(s)
Ying, Yueyang
Advisor(s)
Lee, Yen-Jie
Roland, Gunther
Date Issued
February 2022
Publisher
Massachusetts Institute of Technology
Abstract
The modification of quark- and gluon-initiated jets in the quark-gluon plasma produced in heavy-ion collisions is a long-standing question that has not yet received a definitive answer from experiments. In particular, the size of the modifications differs between theoretical models. Therefore a fully data-driven technique is crucial for an unbiased extraction of the quark and gluon jet spectra and substructure. We demonstrate a fully data-driven method for separating quark and gluon contributions to jet observables using a statistical technique called topic modeling. We will also demonstrate that jet substructures, such as jet shapes and jet fragmentation function, could be extracted using this data-driven method. This proof-of-concept study is based on proton-proton and heavy-ion collision events from the PYQUEN generator with statistics accessible in Run 4 of the Large Hadron Collider. These results suggest the potential for an experimental determination of quark- and gluon-jet spectra and their substructures.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright MIT
Persistent DSpace Link
